Many military planning problems are difficult to solve using pure mathematical programming techniques. One such problem is scheduling unmanned aerial vehicles (UAVs) in military operations subject to dynamic movement and control constraints. This problem is instead formulated as a dynamic programming problem whose approximate solution is obtained via the Assignment Scheduling Capability for UAVs (ASC-U) model using concepts from both simulation and optimization. Optimization is very effective at identifying the best decision for static problems, but is weaker in identifying the best decision in dynamic systems. Simulation is very effective in modeling and capturing dynamic effects, but is weak in optimizing from alternatives. ASC-U exploits the relative strengths of both methodologies by periodically re-optimizing UAV assignments and then having the simulation transition the states according to state dynamics. ASC-U thus exploits the strengths of simulation and optimization to construct good, timely solutions that neither optimization nor simulation could achieve alone.

High-resolution combat models have become so complex that the time necessary to create and analyze a scenario has become unacceptably long. A lower resolution approach to entity-level simulation can complement such models. This paper presents Dynamic Allocation of Fires and Sensors (DAFS), a low-resolution, constructive entity-level simulation framework, that can be rapidly configured and executed. Through the use of a loosely-coupled component architecture, DAFS is extremely flexible and configurable. DAFS allows an analyst to very quickly create a simulation model that captures the first-order effects of a scenario. Although the modeling of entities is done at a low-resolution, DAFS contains some sophisticated capabilities: within the model, commander entities can formulate and solve optimization problems dynamically. DAFS can be used to explore large areas of the parameter space and identify interesting regions where high-resolution models can provide more detailed information.

Information superiority is considered a critical capability for future joint forces. Sensor allocation and information processing are critical to achieving this information superiority but the value of information is difficult to assess. We develop a weighted entropy measure for sensor allocation within simulations by using the Dynamic Model of Situated Cognition as a framework in which to view the processing and flow of information in a complex technological-cognitive system. The entropy measure developed is normalized across each requirement and weighted according to the Commander's priorities within the phase of that operation. We develop a methodology for implementation for this normalized weighted entropy measure to allocate sensors within a combat simulation. INTRODUCTIONDecision-makers struggle with the value of information in almost all forms. Therefore, it is not surprising that the way information is valued and used within combat simulations is also difficult to represent. In this paper we propose a methodology that relies on Commander's Critical Information Requirements (CCIR) that are defined in the planning stage and are linked to phases of combat operations. We particularly do not rely on the notion of the expected value of information which requires calculation but, instead, rely on a measure of uncertainty as it relates to mission priorities, namely, the weighted entropy measure which we describe. Sensor allocation has increased in its importance as the use of sensors has increased with the proliferation of unmanned aerial vehicles, unmanned ground vehicles, unattended ground sensors and others. Methodologies such as the Assignment Scheduling Capability for Unmanned Aerial Vehicles (Ahner 2006) assigns sensors to demands but needs an external mechanism to assign the value received for a sensor-demand assignment. Sensor assets should be allocated based upon the extent that a sensor allocation reduces the uncertainty within this weighted entropy measure. Entropy, when used in the context of information, is often thought of in terms of Shannon's entropy measure which quantifies, in an expected value sense, the quality of long messages, usually in units such as bits. In this paper, we are not interested in the quality of the message but focus on the content of information as it applies to decision making. Nonetheless, we use a weighted entropy measure due to its excellent properties of measuring uncertainty of information. Barr and Sherrill (1996) explore "information gain" in a military context with the addressed primary objective appearing to be "to study relationships between information gained about the enemy disposition and various measures of combat effectiveness (Barr and Sherrill 1996)." A Bayesian update is used to update the probability of an event given the probability that a sensor detects a target. This new probability is used to calculate the new entropy. The difference in the old and new entropy is what is referred to as "information gain." Our paper improves upon this concept by normal...

Information superiority is considered a critical capability for future joint forces. As advances in technology continue to boost our ability to communicate in new and different ways, military forces are restructuring to incorporate these technologies. Yet we are still limited in our ability to measure the contributions made by information networks. We describe three recent studies at the Naval Postgraduate School that involve information networks. First, we examine a simulation model expanded from a two-person, zero-sum game to explore how information superiority contributes to battlefield results and how sensitive it is to information quality. Second, we examine how network-enabled communications affect the logistics operations in a centralized receiving and shipping point. The results are intended to provide operational insights for terminal node operations within a sustainment base. Third, we explore how social networks might be incorporated into agent-based models representing civilian populations in stability operations.

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